Weight-bearing MRI of the Lumbar Spine: Spinal Stenosis and Spondylolisthesis

Hansen, Bjarke Brandt; Nordberg, Cecilie Lerche; Hansen, Philip; Bliddal, Henning; Griffith, James F.; Fournier, Gilles; Thorseth, Ingrid; Guglielmi, Giuseppe

doi:10.1055/s-0039-1697937

“…For spinal lesions, MRI can detect changes such as intervertebral disc degeneration, protrusion, or bulging at an early stage. MRI can observe multiple vertebral bodies and intervertebral discs at a time, which avoids the limitation that other imaging devices can only selectively perform several intervertebral disc imaging [ 6 , 7 ]. However, there are problems such as blurred edges of spinal MRI images, low discrimination with the surrounding areas, inability to show good tissue morphology, and chaotic structural imaging, which make the application of image segmentation technology in spinal MRI imaging more and more [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Spine Medical Image Segmentation Based on Deep Learning

Du

¹

,

Wei

²

,

Liu

³

et al. 2021

Journal of Healthcare Engineering

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The aim was to further explore the clinical value of deep learning algorithm in the field of spinal medical image segmentation, and this study designed an improved U-shaped network (BN-U-Net) algorithm and applied it to the spinal MRI medical image segmentation of 22 research objects. The application value of this algorithm in MRI image processing was comprehensively evaluated by accuracy (Acc), sensitivity (Sen), specificity (Spe), and area under curve (AUC). The results show that the image processing time of fully convolutional network (FCN) algorithm and U-Net algorithm is greater than 6 min, while the processing time of BN-U-Net algorithm is only 5–10 s, and the processing time is significantly shortened ( P < 0.05 ). The Acc, Sen, and Spe results of BN-U-Net segmentation algorithm were 94.54 ± 3.56%, 88.76 ± 2.67%, and 86.27 ± 6.23%, respectively, which were significantly improved compared with FCN algorithm and U-Net algorithm ( P < 0.05 ). In summary, the improved U-Net network algorithm used in this study significantly improves the quality of spinal MRI images by automatic segmentation of MRI images, which is worthy of further promotion in the field of spinal medical image segmentation.

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“…Although the vertebral body is viewed as a solid non-compressive structure without dynamic potential, vertebral body remodeling due to alterations in the degree of biomechanical stress and/or low-grade vertebral body inflammation in the sense of slight concentric body enlargement, slight vertebral body height reduction, and marginal osteophytosis may lead to occurrence and worsening of spinal stenosis when physiological and positional biomechanical changes can provoke neurogenic claudication due to loading of the spine in the upright position (6).…”

Section: Introductionmentioning

confidence: 99%

Effects of axial loaded magnetic resonance imaging of lumbar spine on dural sac and lateral recesses

Bulja

¹

,

Strika

²

,

Jusufbegović

³

et al. 2021

JHSCI

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Introduction: Axial-loaded magnetic resonance imaging (MRI), which can simulate an upright position of the patient may cause a significant reduction of the dural sac cross-sectional area (DCSA) compared with standard MRI, thus providing valuable information in the assessment of the lumbar spinal canal. The purpose of this study was to investigate excessiveness of the change in DCSA and depth of lateral recesses (DLRs) before and after axial-loaded imaging in relation to body mass index (BMI) of the subjects.Methods: Twenty patients were scanned to evaluate DCSA and DLR at three consecutive lumbar spine intervertebral disc levels (L3/4, L4/5, and L5/S1) on conventional-recumbent MRI, and after axial loading were applied.Results: Axial-loaded MRI demonstrates a significant difference of DSCA in comparison to conventional MRI. Furthermore, results show a significant correlation between the DCSA and BMI on level L3/L4, both before and after axial loading MRI. With axial loading, there is a reduction of DSCA of 12.2%, 12.1%, and 2.1% at the levels L3/L4, L4/L5, and L5/S1, respectively. After axial loading has been applied, the depth of the neural foramen has been reduced by an average of 10.1%.Conclusion: Axial-loaded MRI reduces DCSA and DLRs in comparison to standard MRI. Information obtained in this way may be useful to explain the patient’s symptomatology and may provide an additional insight that can influence the treatment decision plan accordingly.

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“…The current imaging diagnostic methods for LSS include X-ray film, computed tomography (CT) examination, and Magnetic Resonance Imaging (MRI) examination [ 5 ], among which X-ray examination measures the transverse diameter of the spinal canal and the sagittal diameter of the spinal canal through the lateral view and the transverse diameter less than 18 mm and the sagittal diameter less than 13 mm is considered spinal stenosis [ 6 ]. CT imaging is an excellent examination method for lumbar and spinal diseases with a high consistence rate with clinical results.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Algorithm-Based Lumbar and Spinal MRI for Evaluation of Efficacy of Chinkuei Shin Chewan Decoction on Lumbar Spinal Stenosis

Zhu

¹

,

Wu

²

,

Wang

³

et al. 2021

Contrast Media & Molecular Imaging

2

0

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The study aimed to explore the application value of lumbar Magnetic Resonance Imaging (MRI) images processed by artificial intelligence algorithms in evaluating the efficacy of chinkuei shin chewan decoction (a traditional Chinese medicine to nourish the kidney) in the treatment of lumbar spinal stenosis (LSS). Specifically, 110 LSS patients admitted to the hospital were selected as the research subjects. They were randomly divided into the control group (n = 55) and experimental group (n = 55) according to different treatment methods. The control group was treated with traditional medicine, and the experimental group additionally took chinkuei shin chewan decoction on its basis. Based on the traditional U-net algorithm, a U-net registration algorithm based on artificial intelligence was designed by introducing the information entropy theory, and the algorithm was applied to the lumbar MRI image evaluation of LSS patients. Compared with the traditional U-net algorithm, the artificial intelligence-based U-net registration algorithm had a decreased noise level P < 0.05 , the Jaccard (J) value (0.84) and the Dice value (0.93) increased significantly versus the traditional algorithm (J = 0.63, Dice = 0.81), and the characteristics of the image were more accurate. Before treatment, the Oswestry Disability Index (ODI) scores of the experimental group and the control group were 44.32 ± 6.45 and 43.32 ± 5.45, respectively. After treatment, the ODI scores of the two groups were 10.21 ± 5.05 and 17.09 ± 5.23, respectively. Both showed significant improvement, while the improvement of the experimental group was more obvious than that of the control group P < 0.05 . The overall effective rates of the two groups of patients were 96.44% and 82.47%, respectively, and the experimental group was significantly higher than the control group P < 0.05 . Under the U-net registration algorithm based on artificial intelligence, the diagnostic accuracy of lumbar MRI in the experimental group was 94.45%, significantly higher than 67.5% before the introduction of the algorithm P < 0.05 . In conclusion, chinkuei shin chewan decoction are effective for the treatment of LSS, and lumbar MRI based on the artificial intelligence U-net registration algorithm can evaluate the efficacy of LSS well and is worthy of promotion.

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Weight-bearing MRI of the Lumbar Spine: Spinal Stenosis and Spondylolisthesis

Cited by 12 publications

References 55 publications

Spine Medical Image Segmentation Based on Deep Learning

Spine Medical Image Segmentation Based on Deep Learning

Effects of axial loaded magnetic resonance imaging of lumbar spine on dural sac and lateral recesses

Artificial Intelligence Algorithm-Based Lumbar and Spinal MRI for Evaluation of Efficacy of Chinkuei Shin Chewan Decoction on Lumbar Spinal Stenosis

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